nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

7. An Interactive Tool for Plan Generation, Inspection, and Visualization

verfasst von : Alfonso E. Gerevini, Alessandro Saetti

Erschienen in: Knowledge Engineering Tools and Techniques for AI Planning

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In mixed-initiative planning systems, humans and AI planners work together for generating satisfactory solution plans or making easier solving hard planning problems, which otherwise would require much greater human planning efforts or much more computational resources. In this approach to plan generation, it is important to have effective plan visualization capabilities, as well to support the user with some interactive capabilities for the human intervention in the planning process. This paper presents an implemented interactive tool for the visualization, generation, and revision of plans. The tool provides an environment through which the user can interact with a state-of-the-art domain-independent planner, and obtain an effective visualization of a rich variety of information during planning, including the reasons why an action is being planned or why its execution in the current plan is expected to fail, the trend of the resource consumption in the plan, and the temporal scheduling of the planned actions. Moreover, the proposed tool supports some ways of human intervention during the planning process to guide the planner towards a solution plan, or to modify the plan under construction and the problem goals.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Modeling Planning Tasks: Representation Matters

Nächstes Kapitel Interactive Visualization in Planning and Scheduling

The word satisficing was coined by Simon to mean “rational enough” [36], and subsequently it was adopted by the optimization community to mean “good enough.” This term has also been adopted by the planning community to indicate planners aimed at computing plans of good quality, but with no guarantee of their optimality w.r.t. a specified plan metric [23].

In PDDL, numerical fluents are functions over real values.

The quality of the plan is automatically measured according to the metric expression specified in the problem formulation. In this example, the quality is expressed by the duration of the plan.

When a search step reaches an LA-graph with no flaw, the planner has found a valid plan. However, this plan is given in output only if its quality improves the quality of the previous output plan. In the example of Fig. 7.9, some valid plans are computed, but only the one found at about the 350th step is given as the third output plan.

Action (load ?p ?t ?a ?l) represents the movement of package ?p from location ?l onto area ?a in truck ?t, while action (unload ?p ?t ?a ?l) represents the opposite movement.

In LPG, the evaluation of the successor LA-graph obtained by removing an action a supporting a precondition g is the estimated number of search steps required to support g by planning actions different from a.

LPG is written in C and is available from http://lpg.ing.unibs.it, while the user interface is written in Java and will soon be made publicly available.

In this experiment, the similarity threshold is set to 1, i.e., the memorized human decisions are reused only if the current neighborhood is the same as the neighborhood previously evaluated by the user.

J. Allen and G. Ferguson, Human-machine collaborative planning, in Proc. of the 3rd Int. NASA Workshop on Planning and Scheduling for Space (2002).

A. Blum and M. Furst, Fast planning through planning graph analysis, in Artificial Intelligence. 90(1997) 281–300.

J. Bresina, A. Jonsson, P. Morris, and R. K. Activity planning for the Mars Exploration Rovers. in Proc. of the 15th Int. Conf. on Automated Planning and Scheduling, (Monterey, California, USA, 2005), pp. 40–49.

M. Cox and C. Zhang, Planning as mixed-initiative goal manipulation, in Proc. of the 15th Int. Conf. on Automated Planning and Scheduling, (Monterey, California, USA, 2005), pp. 282–291.

M. T. Cox and M. Veloso, Supporting Combined Human and Machine Planning, in Proc. of the 2nd Int. Conf. on Case-Based Reasoning, (Providence, Rhode Island, USA), pp. 531–540.

M. T. Cox and M. Veloso, Controlling for unexpected goals when planning in a mixed-initiative setting. in Proc. of the 8th Portuguese Conf. on Artificial Intelligence, (Coimbra, Portugal, 1997), pp. 309–318.

K. Currie and A. Tate, O-plan: the open planning architecture, in Artificial Intelligence. 52(1991):49–86.

Y. Dimopoulos, A. Gerevini, P. Haslum, and A. Saetti, The benchmark domains of the deterministic part of IPC-5, in Abstract Booklet of the competing planners of ICAPS-06, (Cumbria, UK, 2006), pp. 14–19.

P. Eyerich, R. Mattmüller, and G. Röger, Using Context-Enhanced Additive Heuristics for Temporal Numerical Planning, in Proc. of the 19th Int. Conf. on Automated Planning and Scheduling, (Thessaloniki, Greece, 2009), pp. 130–137.

10.

G. Ferguson, J. Allen, and B. Miller, TRAINS-95: Towards a mixed-initiative planning assistant, in Proc. of the 3rd Conf. on Artificial Intelligence Planning Systems, (Edinburgh, UK, 1996) pp. 70–77.

11.

G. Ferguson and J. F. Allen, Arguing about plans: Plan representation and reasoning for mixed-initiative planning, in Proc. of the 2nd Int. Conf. on AI Planning Systems, (Chicago, Illinois, 1994), pp. 43–48.

12.

M. Fox and D. Long, PDDL2.1: An extension to PDDL for expressing temporal planning domains, in Journal of Artificial Intelligence Research. 20(2003):61–124.

13.

A. Gerevini, P. Haslum, D. Long, A. Saetti and Y. Dimopoulos, Deterministic Planning in the Fifth International Planning Competition: PDDL3 and Experimental Evaluation of the Planners, in Artificial Intelligence. 173(2009):619–668.

14.

A. Gerevini, A. Saetti, and I. Serina, Planning through stochastic local search and temporal action graphs, in Journal of Artificial Intelligence Research. 20(2003):239–290.

15.

A. Gerevini, A. Saetti, and I. Serina, An empirical analysis of some heuristic features for local search in LPG, in Proc. of the 14th Int. Conf. on Automated Planning and Scheduling, (Whistler, Canada, 2004), pp. 171–180.

16.

A. Gerevini, A. Saetti, and I. Serina, An approach to temporal planning and scheduling in domains with predictable exogenous events, in Journal of Artificial Intelligence Research. 25(2006):187–231.

17.

A. Gerevini, A. Saetti, and I. Serina, An Approach to Efficient Planning with Numerical Fluents and Multi-Criteria Plan Quality, in Artificial Intelligence. 172(2009):899–944.

18.

A. Gerevini and I. Serina, Fast plan adaptation through planning graphs: Local and systematic search techniques, in Proc. of the 5th Int. Conf. on Artificial Intelligence Planning and Scheduling, (Breckenridge, Colorado, USA, 2000), pp. 112–121.

19.

A. Gerevini and I. Serina, Efficient Plan Adaptation through Replanning Windows and Heuristic Goals, in Journal of Algorithms in Cognition, Informatics and Logic. 102(2010):287–323.

20.

M. Ghallab, A. Howe, C. Knoblock, D. McDermott, A. Ram, M. Veloso, D. Weld, D. Wilkins, PDDL – The Planning Domain Definition Language, CVC TR98-003/DCS TR-1165 (1998), Yale Center for Computational Vision and Control, available at http://cs-www.cs.yale.edu/homes/dvm/,

21.

M. Helmert, The Fast Downward Planning System, in Journal of Artificial Intelligence Research. 26(2006) 191–246.

22.

J. Hoffmann and B. Nebel, The FF Planning System: Fast Plan Generation Through Heuristic Search, in Journal of Artificial Intelligence Research. 14(2001):253–302.

23.

J. Hoffmann and S. Edelkamp, The deterministic part of IPC-4: An overview, in Journal of Artificial Intelligence Research. 24(2005):519–579.

24.

J. Hoffmann, S. Edelkamp, S. Thiebaux, R. Englert, F. Liporace and S. Trueg, Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4, in Journal of Artificial Intelligence Research. 26(2006):453–541.

25.

N. Lino and A. Tate, A visualisation approach for collaborative planning systems based on ontologies, in Proc. of the 8th Int. Conference on Information Visualisation, (London, England, UK, 2004), pp. 807–811.

26.

N. Lino, A. Tate, and Y.-H. Chen-Burger. Semantic support for visualisation in collaborative AI planning. In Proc. of the Workshop on The Role of Ontologies in Planning and Scheduling (2005).

27.

N. Lipovetzky and H. Geffner, Best-First Width Search: Exploration and Exploitation in Classical Planning, in Proc. of the 31st AAAI Conference on Artificial Intelligence, (San Francisco, USA, 2017), pp. 3590–3596.

28.

D. Long and M. Fox, The 3rd international planning competition: Results and analysis, in Journal of Artificial Intelligence Research. 20(2003):1–59.

29.

D. McAllester and D. Rosenblitt, Systematic nonlinear planning, in Proc. of the 9th National Conf. on Artificial Intelligence, (Anaheim, California, USA, 1991), pp. 634–639.

30.

N. Muscettola, HSTS: Integrating Planning and Scheduling, in Intelligent Scheduling, eds. M. Zweben and M.S. Fox (Morgan Kauffmann, San Francisco, USA, 1994), pp. 169–212.

31.

K. L. Myers, P. A. Jarvis, W. M. Tyson, and M. J. Wolverton, A mixed-initiative framework for robust plan sketching, in Proc. of the 13th Int. Conf. on Automated Planning and Scheduling, (Trento, Italy, 2003), pp. 256–265.

32.

X. Nguyen and S. Kambhampati, Reviving partial order planning, in Proc. of the 17th Int. Joint Conf. on Artificial Intelligence, (Seattle, Washington, USA, 2001), pp. 459–464.

33.

J. Penberthy and D. Weld, UCPOP: A sound, complete, partial order planner for ADL, in Proc. of the 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning (Cambridge, Massachusetts, USA, 1992), pp. 103–114.

34.

F. Pommerening, A. Torralba, T. Balyo, The ninth international planning competition (2018), https://ipc2018.bitbucket.io

35.

S. Richter, M. Westphal, The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks, in Journal of Artificial Intelligence Research, 29(2010):127–177.

36.

H. A. Simon, Models of Man, (John Wiley & Sons Inc., New York, USA, 1957).

37.

A. Tate, In Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative, (AAAI Press, Menlo Park, California, USA, 1996).

38.

G. Tecuci, Proc. of the IJCAI Workshop on Mixed-Initiative Intelligent Systems, (AAAI Press, Menlo Park, California, USA, 2003).

39.

M. Veloso, M. Mulvehill, A., and T. Cox, M, Rationale-supported mixed-initiative case-based planning, in Proc. of the 9th Conf. on Innovative Applications of Artificial Intelligence, (Providence, Rhode Island, USA, 1997), pp. 1072–1077.

40.

C. Zhang, Cognitive models for mixed-initiative planning, (PhD thesis, Wright State University, Computer Science and Engineering Department, Dayton, Ohio, USA, 2002).

Titel: An Interactive Tool for Plan Generation, Inspection, and Visualization
verfasst von: Alfonso E. Gerevini
Alessandro Saetti
Verlag: Springer International Publishing
Buch: Knowledge Engineering Tools and Techniques for AI Planning
Print ISBN: 978-3-030-38560-6

Electronic ISBN: 978-3-030-38561-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-38561-3_7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"